Machine-learning model that recommends virtual experiences based on graphs and clustering

A computer-implemented method to recommend a ranked set of virtual experiences to a user, the method comprising:

• receiving, with a first machine-learning model, training data that includes pairs of users and virtual experiences, wherein each user of a pair is associated with user features, each virtual experience of the pair is associated with item features, and each pair includes a virtual experience that a corresponding user interacted with;
• training a user tower of the first machine-learning model by:
• training a first deep neural network (DNN) to output user embeddings;
• and
• generating user clusters from the user embeddings based on a distance between each of the user embeddings;
• training an item tower of the first machine-learning model by:
• training a second DNN to output item embeddings;
• and
• generating item clusters from the item embeddings based on a distance between each of the item embeddings;
• wherein the first machine-learning model is trained to generate candidate virtual experiences for a user;
• and
• training a second machine-learning model to recommend a ranked set of virtual experiences to a user by:
• receiving the user clusters and the item clusters from the second machine-learning model;
• generating feature embeddings based on item features, user features, the user clusters, and the item clusters;
• and
• training a third DNN to output a ranked subset of the candidate virtual experiences based on the feature embeddings.

A recommendation system comprising:

• one or more processors;
• and
• a memory coupled to the one or more processors, with instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• providing user features to a trained neural network including an item tower and a user tower, wherein the user features include a past user interaction history with one or more virtual experiences;
• outputting, with the trained neural network, candidate virtual experiences that are based on the user features, user clusters, and item clusters;
• providing the candidate virtual experiences as input to a trained ranking model, wherein the trained ranking model is trained based on the user clusters and the item clusters associated with the trained neural network;
• and
• outputting, with the trained ranking model, a ranked subset of the candidate virtual experiences.